{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1dfd099a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "771d7f3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/user/conda/envs/dpf/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "100%|██████████| 3/3 [00:00<00:00, 318.39it/s]\n"
     ]
    }
   ],
   "source": [
    "from DPF import ShardsDatasetConfig, DatasetReader\n",
    "\n",
    "config = ShardsDatasetConfig.from_path_and_columns(\n",
    "    'example_dataset',\n",
    "    image_name_col='image_name',\n",
    "    text_col=\"caption\"\n",
    ")\n",
    "\n",
    "reader = DatasetReader()\n",
    "processor = reader.read_from_config(config)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab35c635",
   "metadata": {},
   "source": [
    "## Check dataset samples and summary info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0747f47c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset format: shards\n",
      "Path: example_dataset\n",
      "Modalities: ['image', 'text']\n",
      "Columns: 3\n",
      "Total samples: 500\n"
     ]
    }
   ],
   "source": [
    "processor.print_summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1b194714",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>text</th>\n",
       "      <th>split_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>example_dataset/0.tar/0.jpg</td>\n",
       "      <td>шотландцы в национальной одежде с флагом. - ba...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>example_dataset/0.tar/1.jpg</td>\n",
       "      <td>королевская золотая корона с драгоценностями н...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>example_dataset/0.tar/2.jpg</td>\n",
       "      <td>Пасха</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>example_dataset/0.tar/3.jpg</td>\n",
       "      <td>округа нью-джерси карта печати - elizabeth sto...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>example_dataset/0.tar/4.jpg</td>\n",
       "      <td>английская золотая корона с драгоценностями, и...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>example_dataset/2.tar/495.jpg</td>\n",
       "      <td>bokeh Background</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>example_dataset/2.tar/496.jpg</td>\n",
       "      <td>Healthcare, Medicine,</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>example_dataset/2.tar/497.jpg</td>\n",
       "      <td>A set of business teams icons that include edi...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>example_dataset/2.tar/498.jpg</td>\n",
       "      <td>Vector floral pattern icon collection</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>example_dataset/2.tar/499.jpg</td>\n",
       "      <td>Set of Brown ribbons, banners, badges and labe...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        image_path  \\\n",
       "0      example_dataset/0.tar/0.jpg   \n",
       "1      example_dataset/0.tar/1.jpg   \n",
       "2      example_dataset/0.tar/2.jpg   \n",
       "3      example_dataset/0.tar/3.jpg   \n",
       "4      example_dataset/0.tar/4.jpg   \n",
       "..                             ...   \n",
       "495  example_dataset/2.tar/495.jpg   \n",
       "496  example_dataset/2.tar/496.jpg   \n",
       "497  example_dataset/2.tar/497.jpg   \n",
       "498  example_dataset/2.tar/498.jpg   \n",
       "499  example_dataset/2.tar/499.jpg   \n",
       "\n",
       "                                                  text split_name  \n",
       "0    шотландцы в национальной одежде с флагом. - ba...          0  \n",
       "1    королевская золотая корона с драгоценностями н...          0  \n",
       "2                                                Пасха          0  \n",
       "3    округа нью-джерси карта печати - elizabeth sto...          0  \n",
       "4    английская золотая корона с драгоценностями, и...          0  \n",
       "..                                                 ...        ...  \n",
       "495                                   bokeh Background          2  \n",
       "496                              Healthcare, Medicine,          2  \n",
       "497  A set of business teams icons that include edi...          2  \n",
       "498              Vector floral pattern icon collection          2  \n",
       "499  Set of Brown ribbons, banners, badges and labe...          2  \n",
       "\n",
       "[500 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0231344f",
   "metadata": {},
   "source": [
    "# Filters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d61f5208",
   "metadata": {},
   "source": [
    "## Image info filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c50f3a2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:00<00:00, 588.61it/s]\n"
     ]
    }
   ],
   "source": [
    "from DPF.filters.images.info_filter import ImageInfoFilter\n",
    "\n",
    "datafilter = ImageInfoFilter(workers=16)\n",
    "processor.apply_data_filter(datafilter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6e500cff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>text</th>\n",
       "      <th>split_name</th>\n",
       "      <th>is_correct</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>channels</th>\n",
       "      <th>error</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>example_dataset/0.tar/0.jpg</td>\n",
       "      <td>шотландцы в национальной одежде с флагом. - ba...</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>612</td>\n",
       "      <td>612</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>example_dataset/0.tar/1.jpg</td>\n",
       "      <td>королевская золотая корона с драгоценностями н...</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>612</td>\n",
       "      <td>306</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>example_dataset/0.tar/2.jpg</td>\n",
       "      <td>Пасха</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>640</td>\n",
       "      <td>360</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>example_dataset/0.tar/3.jpg</td>\n",
       "      <td>округа нью-джерси карта печати - elizabeth sto...</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>464</td>\n",
       "      <td>612</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>example_dataset/0.tar/4.jpg</td>\n",
       "      <td>английская золотая корона с драгоценностями, и...</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>612</td>\n",
       "      <td>612</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>example_dataset/2.tar/495.jpg</td>\n",
       "      <td>bokeh Background</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>612</td>\n",
       "      <td>344</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>example_dataset/2.tar/496.jpg</td>\n",
       "      <td>Healthcare, Medicine,</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>612</td>\n",
       "      <td>612</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>example_dataset/2.tar/497.jpg</td>\n",
       "      <td>A set of business teams icons that include edi...</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>612</td>\n",
       "      <td>612</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>example_dataset/2.tar/498.jpg</td>\n",
       "      <td>Vector floral pattern icon collection</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>612</td>\n",
       "      <td>612</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>example_dataset/2.tar/499.jpg</td>\n",
       "      <td>Set of Brown ribbons, banners, badges and labe...</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>612</td>\n",
       "      <td>612</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        image_path  \\\n",
       "0      example_dataset/0.tar/0.jpg   \n",
       "1      example_dataset/0.tar/1.jpg   \n",
       "2      example_dataset/0.tar/2.jpg   \n",
       "3      example_dataset/0.tar/3.jpg   \n",
       "4      example_dataset/0.tar/4.jpg   \n",
       "..                             ...   \n",
       "495  example_dataset/2.tar/495.jpg   \n",
       "496  example_dataset/2.tar/496.jpg   \n",
       "497  example_dataset/2.tar/497.jpg   \n",
       "498  example_dataset/2.tar/498.jpg   \n",
       "499  example_dataset/2.tar/499.jpg   \n",
       "\n",
       "                                                  text split_name  is_correct  \\\n",
       "0    шотландцы в национальной одежде с флагом. - ba...          0        True   \n",
       "1    королевская золотая корона с драгоценностями н...          0        True   \n",
       "2                                                Пасха          0        True   \n",
       "3    округа нью-джерси карта печати - elizabeth sto...          0        True   \n",
       "4    английская золотая корона с драгоценностями, и...          0        True   \n",
       "..                                                 ...        ...         ...   \n",
       "495                                   bokeh Background          2        True   \n",
       "496                              Healthcare, Medicine,          2        True   \n",
       "497  A set of business teams icons that include edi...          2        True   \n",
       "498              Vector floral pattern icon collection          2        True   \n",
       "499  Set of Brown ribbons, banners, badges and labe...          2        True   \n",
       "\n",
       "     width  height  channels error  \n",
       "0      612     612         3  None  \n",
       "1      612     306         3  None  \n",
       "2      640     360         3  None  \n",
       "3      464     612         3  None  \n",
       "4      612     612         3  None  \n",
       "..     ...     ...       ...   ...  \n",
       "495    612     344         3  None  \n",
       "496    612     612         3  None  \n",
       "497    612     612         3  None  \n",
       "498    612     612         3  None  \n",
       "499    612     612         3  None  \n",
       "\n",
       "[500 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.df # new columns is_correct, width, height, channels, error are added\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c66a489a",
   "metadata": {},
   "source": [
    "## PHash filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c803322d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:01<00:00, 345.42it/s]\n"
     ]
    }
   ],
   "source": [
    "from DPF.filters.images.hash_filters import PHashFilter\n",
    "\n",
    "datafilter = PHashFilter(sim_hash_size=8, workers=16)\n",
    "processor.apply_data_filter(datafilter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0003049b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      e1f6e7c0e07f0e3d381f8fc07e09c7f0103fe07038ec7918\n",
       "1      fc3100ffcd0b6301c8ef9f1c0399d8030ebfe38e047db03f\n",
       "2      effe38e3be07747080a1d9d8fe2fe80081f8ff8c2701f018\n",
       "3      ec4007e06e0113b7f8f007e603f63f1381d81f8fff027e33\n",
       "4      e3f1c6ec703ffc01b81b9fc0216e0703efc2fc01b1fa81f0\n",
       "                             ...                       \n",
       "495    e431028fcd3581f1e6f073071df63df139c303b3d0f23417\n",
       "496    fb8efe188cc61869c6187c6618f9c019ec4618fc771c8ff8\n",
       "497    fc70381c70381c71f81c71f81c70385fbe07e38fc7e38fc0\n",
       "498    e80f7f080d3c0fc13e0c4770f03ec5f38281f7e1bbe0b1c7\n",
       "499    fbf03803f03ce001fc1b8fc103fe073c7e38fc01b8e381df\n",
       "Name: image_phash_8, Length: 500, dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.df['image_phash_8'] # Perceptual hash of images\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "634e0d4d",
   "metadata": {},
   "source": [
    "## ImprovedAestheticFilter (from LAION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f714f436",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['improved_aesthetic_score_ViT-L/14']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10it [00:05,  1.95it/s]                      \n"
     ]
    }
   ],
   "source": [
    "from DPF.filters.images.aesthetic_improved_filter import ImprovedAestheticFilter\n",
    "\n",
    "datafilter = ImprovedAestheticFilter(\n",
    "    weights_folder='../weights',  # path to weights folder, will be downloaded to this folder\n",
    "    device='cuda:0',\n",
    "    workers=16\n",
    ")\n",
    "print(datafilter.result_columns)\n",
    "processor.apply_data_filter(datafilter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dd16e52a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5.601146\n",
       "1      4.966230\n",
       "2      5.068741\n",
       "3      4.848436\n",
       "4      5.539761\n",
       "         ...   \n",
       "495    5.285481\n",
       "496    4.501980\n",
       "497    3.883074\n",
       "498    5.069400\n",
       "499    4.538239\n",
       "Name: improved_aesthetic_score_ViT-L/14, Length: 500, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.df['improved_aesthetic_score_ViT-L/14']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "111a69de",
   "metadata": {},
   "source": [
    "## CLIPLabelsFilter "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6d838328",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ViT-L/14 clip score \"photo of a car\"', 'ViT-L/14 clip score \"photo of a man\"', 'ViT-L/14 clip score \"photo of a dog\"']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10it [00:03,  2.52it/s]                      \n"
     ]
    }
   ],
   "source": [
    "from DPF.filters.images.cliplabels_filter import CLIPLabelsFilter\n",
    "\n",
    "datafilter = CLIPLabelsFilter(\n",
    "    clip_model='ViT-L/14',\n",
    "    labels=['photo of a car', 'photo of a man', 'photo of a dog'],\n",
    "    weights_folder='../weights', # path to weights folder, will be downloaded to this folder\n",
    "    device='cuda:0',\n",
    "    workers=16\n",
    ")\n",
    "print(datafilter.result_columns)\n",
    "processor.apply_data_filter(datafilter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "08a39d41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.049438\n",
       "1      0.090820\n",
       "2      0.124451\n",
       "3      0.110779\n",
       "4      0.104065\n",
       "         ...   \n",
       "495    0.134888\n",
       "496    0.046631\n",
       "497    0.003983\n",
       "498    0.075867\n",
       "499    0.063782\n",
       "Name: ViT-L/14 clip score \"photo of a dog\", Length: 500, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colname = 'ViT-L/14 clip score \"photo of a dog\"'\n",
    "processor.df[colname]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f82177fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "image/png": 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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=612x408>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import io\n",
    "\n",
    "m2d, metadata = processor.get_random_sample(processor.df[colname]>0.22)\n",
    "Image.open(io.BytesIO(m2d['image']))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecd5e17d",
   "metadata": {},
   "source": [
    "## WatermarksFilter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "16f7dce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/.mlspace/envs/dpf_llava/lib/python3.11/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/jovyan/.mlspace/envs/dpf_llava/lib/python3.11/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
      "  warnings.warn(msg)\n",
      "/home/jovyan/.mlspace/envs/dpf_llava/lib/python3.11/site-packages/huggingface_hub/file_download.py:669: FutureWarning: 'cached_download' is the legacy way to download files from the HF hub, please consider upgrading to 'hf_hub_download'\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['watermark_resnext101_32x8d-large']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10it [00:04,  2.42it/s]                      \n"
     ]
    }
   ],
   "source": [
    "from DPF.filters.images.watermarks_filter import WatermarksFilter\n",
    "\n",
    "datafilter = WatermarksFilter(\n",
    "    watermarks_model='resnext101_32x8d-large',\n",
    "    weights_folder='../weights', # path to weights folder, will be downloaded to this folder\n",
    "    device='cuda:0',\n",
    "    workers=16\n",
    ")\n",
    "print(datafilter.result_columns)\n",
    "processor.apply_data_filter(datafilter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e0bfacd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "495    1\n",
       "496    1\n",
       "497    1\n",
       "498    1\n",
       "499    1\n",
       "Name: watermark_resnext101_32x8d-large, Length: 500, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.df['watermark_resnext101_32x8d-large']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed895260",
   "metadata": {},
   "source": [
    "## Text detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0cd9e560",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-22 13:34:51.322120: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-05-22 13:34:52.049444: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "/home/user/conda/envs/dpf/lib/python3.11/site-packages/huggingface_hub/file_download.py:671: FutureWarning: 'cached_download' is the legacy way to download files from the HF hub, please consider upgrading to 'hf_hub_download'\n",
      "  warnings.warn(\n",
      "/home/user/conda/envs/dpf/lib/python3.11/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
      "  warnings.warn(\n",
      "/home/user/conda/envs/dpf/lib/python3.11/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['text_boxes', 'num_text_boxes', 'text_area']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|▏         | 7/500 [00:01<00:53,  9.14it/s]/home/user/conda/envs/dpf/lib/python3.11/site-packages/torch/nn/modules/conv.py:456: UserWarning: Plan failed with a cudnnException: CUDNN_BACKEND_EXECUTION_PLAN_DESCRIPTOR: cudnnFinalize Descriptor Failed cudnn_status: CUDNN_STATUS_NOT_SUPPORTED (Triggered internally at ../aten/src/ATen/native/cudnn/Conv_v8.cpp:919.)\n",
      "  return F.conv2d(input, weight, bias, self.stride,\n",
      "100%|██████████| 500/500 [00:14<00:00, 35.06it/s]\n"
     ]
    }
   ],
   "source": [
    "from DPF.filters.images.text_detection_filter import CRAFTFilter\n",
    "\n",
    "datafilter = CRAFTFilter(\n",
    "    '../weights',\n",
    "    device=\"cuda:0\",\n",
    "    workers=16\n",
    ")\n",
    "print(datafilter.result_columns)\n",
    "processor.apply_data_filter(datafilter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d2c61cf0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text_boxes</th>\n",
       "      <th>num_text_boxes</th>\n",
       "      <th>text_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[[[226, 93], [252, 101]], [[276, 104], [304, 1...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.018773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>[[[461, 17], [518, 58]], [[42, 24], [162, 50]]...</td>\n",
       "      <td>13</td>\n",
       "      <td>0.147358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>[[[511, 33], [587, 57]], [[510, 62], [589, 90]...</td>\n",
       "      <td>3</td>\n",
       "      <td>0.012431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>[[[27, 20], [147, 588]], [[506, 29], [550, 36]...</td>\n",
       "      <td>15</td>\n",
       "      <td>0.229978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            text_boxes  num_text_boxes  \\\n",
       "0    [[[161, 310], [170, 321]], [[188, 309], [198, ...               9   \n",
       "1                                                   []               0   \n",
       "2                                                   []               0   \n",
       "3    [[[226, 93], [252, 101]], [[276, 104], [304, 1...              20   \n",
       "4                                                   []               0   \n",
       "..                                                 ...             ...   \n",
       "495                                                 []               0   \n",
       "496  [[[461, 17], [518, 58]], [[42, 24], [162, 50]]...              13   \n",
       "497  [[[511, 33], [587, 57]], [[510, 62], [589, 90]...               3   \n",
       "498                                                 []               0   \n",
       "499  [[[27, 20], [147, 588]], [[506, 29], [550, 36]...              15   \n",
       "\n",
       "     text_area  \n",
       "0     0.013609  \n",
       "1     0.000000  \n",
       "2     0.000000  \n",
       "3     0.018773  \n",
       "4     0.000000  \n",
       "..         ...  \n",
       "495   0.000000  \n",
       "496   0.147358  \n",
       "497   0.012431  \n",
       "498   0.000000  \n",
       "499   0.229978  \n",
       "\n",
       "[500 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.df[datafilter.result_columns]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ac9bc86",
   "metadata": {},
   "source": [
    "# Captioning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb18b92c",
   "metadata": {},
   "source": [
    "## LLaVaCaptioningFilter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84243b0b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Describe this image and its style in a very detailed manner\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52e33cea8deb4448ba1766523d7fcb84",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at openai/clip-vit-large-patch14-336 were not used when initializing CLIPVisionModel: ['text_model.encoder.layers.8.self_attn.v_proj.bias', 'text_model.encoder.layers.8.layer_norm1.bias', 'text_model.encoder.layers.7.layer_norm1.weight', 'text_model.encoder.layers.4.mlp.fc2.bias', 'text_model.encoder.layers.2.layer_norm2.bias', 'text_model.encoder.layers.3.self_attn.q_proj.weight', 'text_model.encoder.layers.4.self_attn.out_proj.weight', 'text_model.encoder.layers.2.mlp.fc1.bias', 'text_model.encoder.layers.8.self_attn.out_proj.weight', 'text_model.encoder.layers.1.self_attn.v_proj.weight', 'text_model.encoder.layers.2.self_attn.q_proj.weight', 'text_model.encoder.layers.0.mlp.fc2.weight', 'text_model.encoder.layers.0.layer_norm1.bias', 'text_model.encoder.layers.4.self_attn.q_proj.bias', 'text_model.encoder.layers.6.self_attn.q_proj.bias', 'visual_projection.weight', 'text_model.encoder.layers.4.mlp.fc1.weight', 'text_model.encoder.layers.5.layer_norm2.bias', 'text_model.encoder.layers.10.self_attn.q_proj.bias', 'text_model.encoder.layers.1.layer_norm2.bias', 'text_model.encoder.layers.9.layer_norm2.bias', 'text_model.encoder.layers.8.mlp.fc1.weight', 'text_model.encoder.layers.1.self_attn.q_proj.bias', 'text_model.final_layer_norm.bias', 'text_model.encoder.layers.4.self_attn.v_proj.bias', 'text_model.encoder.layers.8.mlp.fc2.weight', 'text_model.encoder.layers.6.self_attn.k_proj.weight', 'text_model.encoder.layers.3.self_attn.out_proj.weight', 'text_model.encoder.layers.5.self_attn.q_proj.bias', 'text_model.encoder.layers.3.mlp.fc2.bias', 'text_model.encoder.layers.2.self_attn.out_proj.bias', 'text_model.encoder.layers.4.layer_norm2.bias', 'text_projection.weight', 'text_model.encoder.layers.4.self_attn.q_proj.weight', 'text_model.encoder.layers.0.mlp.fc2.bias', 'text_model.encoder.layers.10.layer_norm2.bias', 'text_model.encoder.layers.4.self_attn.v_proj.weight', 'text_model.encoder.layers.6.self_attn.v_proj.weight', 'text_model.encoder.layers.6.layer_norm2.weight', 'text_model.encoder.layers.9.self_attn.v_proj.bias', 'text_model.encoder.layers.9.mlp.fc1.bias', 'text_model.encoder.layers.0.self_attn.out_proj.weight', 'text_model.encoder.layers.10.self_attn.v_proj.weight', 'text_model.encoder.layers.5.layer_norm1.weight', 'text_model.encoder.layers.11.self_attn.q_proj.bias', 'text_model.encoder.layers.6.mlp.fc2.bias', 'text_model.encoder.layers.3.self_attn.k_proj.weight', 'text_model.encoder.layers.5.mlp.fc2.bias', 'text_model.encoder.layers.7.self_attn.out_proj.bias', 'text_model.encoder.layers.6.self_attn.v_proj.bias', 'text_model.encoder.layers.9.layer_norm2.weight', 'text_model.encoder.layers.6.self_attn.out_proj.weight', 'text_model.encoder.layers.0.self_attn.v_proj.bias', 'text_model.encoder.layers.8.self_attn.q_proj.weight', 'text_model.encoder.layers.2.mlp.fc2.weight', 'text_model.encoder.layers.11.self_attn.k_proj.weight', 'text_model.encoder.layers.5.self_attn.v_proj.bias', 'text_model.encoder.layers.8.self_attn.out_proj.bias', 'text_model.encoder.layers.0.self_attn.k_proj.weight', 'text_model.encoder.layers.11.layer_norm1.bias', 'text_model.encoder.layers.9.mlp.fc2.weight', 'text_model.encoder.layers.9.self_attn.k_proj.bias', 'text_model.encoder.layers.1.self_attn.q_proj.weight', 'text_model.encoder.layers.11.self_attn.k_proj.bias', 'text_model.encoder.layers.1.layer_norm1.bias', 'text_model.encoder.layers.1.layer_norm1.weight', 'text_model.encoder.layers.1.layer_norm2.weight', 'text_model.encoder.layers.10.self_attn.q_proj.weight', 'text_model.encoder.layers.8.mlp.fc2.bias', 'logit_scale', 'text_model.encoder.layers.2.layer_norm1.bias', 'text_model.encoder.layers.1.mlp.fc1.weight', 'text_model.encoder.layers.9.mlp.fc2.bias', 'text_model.encoder.layers.3.self_attn.k_proj.bias', 'text_model.encoder.layers.3.self_attn.v_proj.bias', 'text_model.encoder.layers.7.mlp.fc2.weight', 'text_model.encoder.layers.7.self_attn.v_proj.bias', 'text_model.encoder.layers.9.self_attn.q_proj.bias', 'text_model.encoder.layers.1.mlp.fc1.bias', 'text_model.encoder.layers.7.layer_norm1.bias', 'text_model.encoder.layers.3.mlp.fc1.bias', 'text_model.encoder.layers.9.layer_norm1.weight', 'text_model.encoder.layers.8.self_attn.v_proj.weight', 'text_model.encoder.layers.2.self_attn.out_proj.weight', 'text_model.encoder.layers.6.self_attn.k_proj.bias', 'text_model.encoder.layers.11.mlp.fc2.weight', 'text_model.encoder.layers.8.layer_norm1.weight', 'text_model.embeddings.position_ids', 'text_model.encoder.layers.1.self_attn.out_proj.weight', 'text_model.encoder.layers.11.self_attn.out_proj.weight', 'text_model.encoder.layers.9.layer_norm1.bias', 'text_model.encoder.layers.3.mlp.fc2.weight', 'text_model.encoder.layers.8.mlp.fc1.bias', 'text_model.encoder.layers.5.mlp.fc1.bias', 'text_model.encoder.layers.3.self_attn.v_proj.weight', 'text_model.encoder.layers.7.mlp.fc1.weight', 'text_model.encoder.layers.5.mlp.fc1.weight', 'text_model.encoder.layers.9.self_attn.q_proj.weight', 'text_model.encoder.layers.7.self_attn.out_proj.weight', 'text_model.encoder.layers.6.layer_norm1.weight', 'text_model.encoder.layers.4.self_attn.out_proj.bias', 'text_model.encoder.layers.8.self_attn.k_proj.weight', 'text_model.encoder.layers.4.layer_norm1.weight', 'text_model.encoder.layers.0.mlp.fc1.weight', 'text_model.encoder.layers.0.layer_norm1.weight', 'text_model.embeddings.token_embedding.weight', 'text_model.encoder.layers.4.self_attn.k_proj.bias', 'text_model.encoder.layers.8.self_attn.k_proj.bias', 'text_model.encoder.layers.7.layer_norm2.bias', 'text_model.encoder.layers.1.self_attn.out_proj.bias', 'text_model.encoder.layers.7.self_attn.k_proj.bias', 'text_model.encoder.layers.6.self_attn.out_proj.bias', 'text_model.encoder.layers.3.layer_norm2.bias', 'text_model.encoder.layers.10.mlp.fc1.bias', 'text_model.encoder.layers.11.mlp.fc2.bias', 'text_model.encoder.layers.0.self_attn.q_proj.weight', 'text_model.encoder.layers.6.self_attn.q_proj.weight', 'text_model.encoder.layers.0.layer_norm2.bias', 'text_model.encoder.layers.7.self_attn.q_proj.bias', 'text_model.encoder.layers.5.self_attn.k_proj.bias', 'text_model.encoder.layers.1.mlp.fc2.bias', 'text_model.encoder.layers.1.mlp.fc2.weight', 'text_model.encoder.layers.10.mlp.fc2.weight', 'text_model.encoder.layers.10.layer_norm2.weight', 'text_model.encoder.layers.10.mlp.fc2.bias', 'text_model.encoder.layers.5.self_attn.out_proj.bias', 'text_model.encoder.layers.6.mlp.fc1.weight', 'text_model.encoder.layers.11.layer_norm2.bias', 'text_model.encoder.layers.10.mlp.fc1.weight', 'text_model.encoder.layers.5.layer_norm2.weight', 'text_model.encoder.layers.11.self_attn.q_proj.weight', 'text_model.encoder.layers.10.self_attn.v_proj.bias', 'text_model.encoder.layers.0.layer_norm2.weight', 'text_model.encoder.layers.7.self_attn.q_proj.weight', 'text_model.encoder.layers.2.self_attn.v_proj.bias', 'text_model.encoder.layers.8.layer_norm2.weight', 'text_model.encoder.layers.4.mlp.fc1.bias', 'text_model.encoder.layers.3.layer_norm2.weight', 'text_model.embeddings.position_embedding.weight', 'text_model.encoder.layers.4.layer_norm1.bias', 'text_model.final_layer_norm.weight', 'text_model.encoder.layers.2.self_attn.k_proj.weight', 'text_model.encoder.layers.11.layer_norm1.weight', 'text_model.encoder.layers.3.layer_norm1.bias', 'text_model.encoder.layers.11.mlp.fc1.weight', 'text_model.encoder.layers.10.self_attn.out_proj.weight', 'text_model.encoder.layers.0.self_attn.v_proj.weight', 'text_model.encoder.layers.3.self_attn.out_proj.bias', 'text_model.encoder.layers.6.layer_norm2.bias', 'text_model.encoder.layers.8.layer_norm2.bias', 'text_model.encoder.layers.2.layer_norm2.weight', 'text_model.encoder.layers.1.self_attn.k_proj.bias', 'text_model.encoder.layers.8.self_attn.q_proj.bias', 'text_model.encoder.layers.10.layer_norm1.weight', 'text_model.encoder.layers.2.self_attn.k_proj.bias', 'text_model.encoder.layers.0.self_attn.k_proj.bias', 'text_model.encoder.layers.5.self_attn.q_proj.weight', 'text_model.encoder.layers.10.layer_norm1.bias', 'text_model.encoder.layers.3.self_attn.q_proj.bias', 'text_model.encoder.layers.10.self_attn.k_proj.weight', 'text_model.encoder.layers.2.mlp.fc1.weight', 'text_model.encoder.layers.7.mlp.fc1.bias', 'text_model.encoder.layers.5.self_attn.out_proj.weight', 'text_model.encoder.layers.9.self_attn.k_proj.weight', 'text_model.encoder.layers.1.self_attn.k_proj.weight', 'text_model.encoder.layers.7.mlp.fc2.bias', 'text_model.encoder.layers.6.mlp.fc2.weight', 'text_model.encoder.layers.11.layer_norm2.weight', 'text_model.encoder.layers.4.layer_norm2.weight', 'text_model.encoder.layers.2.layer_norm1.weight', 'text_model.encoder.layers.4.mlp.fc2.weight', 'text_model.encoder.layers.9.mlp.fc1.weight', 'text_model.encoder.layers.11.mlp.fc1.bias', 'text_model.encoder.layers.11.self_attn.out_proj.bias', 'text_model.encoder.layers.11.self_attn.v_proj.bias', 'text_model.encoder.layers.2.mlp.fc2.bias', 'text_model.encoder.layers.9.self_attn.out_proj.weight', 'text_model.encoder.layers.7.self_attn.v_proj.weight', 'text_model.encoder.layers.0.self_attn.q_proj.bias', 'text_model.encoder.layers.5.layer_norm1.bias', 'text_model.encoder.layers.5.mlp.fc2.weight', 'text_model.encoder.layers.10.self_attn.out_proj.bias', 'text_model.encoder.layers.0.self_attn.out_proj.bias', 'text_model.encoder.layers.5.self_attn.v_proj.weight', 'text_model.encoder.layers.2.self_attn.q_proj.bias', 'text_model.encoder.layers.9.self_attn.out_proj.bias', 'text_model.encoder.layers.3.layer_norm1.weight', 'text_model.encoder.layers.0.mlp.fc1.bias', 'text_model.encoder.layers.7.layer_norm2.weight', 'text_model.encoder.layers.3.mlp.fc1.weight', 'text_model.encoder.layers.10.self_attn.k_proj.bias', 'text_model.encoder.layers.11.self_attn.v_proj.weight', 'text_model.encoder.layers.6.mlp.fc1.bias', 'text_model.encoder.layers.2.self_attn.v_proj.weight', 'text_model.encoder.layers.9.self_attn.v_proj.weight', 'text_model.encoder.layers.1.self_attn.v_proj.bias', 'text_model.encoder.layers.7.self_attn.k_proj.weight', 'text_model.encoder.layers.4.self_attn.k_proj.weight', 'text_model.encoder.layers.5.self_attn.k_proj.weight', 'text_model.encoder.layers.6.layer_norm1.bias']\n",
      "- This IS expected if you are initializing CLIPVisionModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing CLIPVisionModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 63/63 [09:56<00:00,  9.48s/it]\n"
     ]
    }
   ],
   "source": [
    "from DPF.filters.images.llava_captioning_filter import LLaVaCaptioningFilter\n",
    "\n",
    "datafilter = LLaVaCaptioningFilter(\n",
    "    workers=16, \n",
    "    prompt='pixart', \n",
    "    batch_size=8, \n",
    "    device='cuda:0'\n",
    ")\n",
    "\n",
    "processor.apply_data_filter(datafilter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0f7ab96",
   "metadata": {},
   "outputs": [],
   "source": [
    "processor.df['caption liuhaotian/llava-v1.5-13b prompt pixart']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76d24a11",
   "metadata": {},
   "source": [
    "## BLIP captioning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9571be07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-05-22 13:41:44,754] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/user/conda/envs/dpf/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
      "  warnings.warn(\n",
      "18it [00:50,  2.79s/it]                        \n"
     ]
    }
   ],
   "source": [
    "from DPF.filters.images.blip_captioning_filter import BLIPCaptioningFilter\n",
    "\n",
    "datafilter = BLIPCaptioningFilter(\n",
    "    workers=16, \n",
    "    batch_size=32, \n",
    "    device='cuda:0'\n",
    ")\n",
    "\n",
    "processor.apply_data_filter(datafilter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "86e3e38b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         a man and a woman dressed in scottish clothing\n",
       "1               a british flag with a crown on top of it\n",
       "2         a couple of people that are painting some eggs\n",
       "3                       a map of the state of new jersey\n",
       "4      a purple and gold crown sitting on top of a wh...\n",
       "                             ...                        \n",
       "495             a blurry image of rain drops on a window\n",
       "496              a large set of medical and health icons\n",
       "497                  a large set of line icons of people\n",
       "498    a bunch of different colored circles on a whit...\n",
       "499                  a set of various ribbons and badges\n",
       "Name: blip_caption, Length: 500, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.df['blip_caption']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e0bab4e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>blip_caption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>шотландцы в национальной одежде с флагом. - ba...</td>\n",
       "      <td>a man and a woman dressed in scottish clothing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>королевская золотая корона с драгоценностями н...</td>\n",
       "      <td>a british flag with a crown on top of it</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Пасха</td>\n",
       "      <td>a couple of people that are painting some eggs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>округа нью-джерси карта печати - elizabeth sto...</td>\n",
       "      <td>a map of the state of new jersey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>английская золотая корона с драгоценностями, и...</td>\n",
       "      <td>a purple and gold crown sitting on top of a wh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>bokeh Background</td>\n",
       "      <td>a blurry image of rain drops on a window</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>Healthcare, Medicine,</td>\n",
       "      <td>a large set of medical and health icons</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>A set of business teams icons that include edi...</td>\n",
       "      <td>a large set of line icons of people</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>Vector floral pattern icon collection</td>\n",
       "      <td>a bunch of different colored circles on a whit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>Set of Brown ribbons, banners, badges and labe...</td>\n",
       "      <td>a set of various ribbons and badges</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>500 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  text  \\\n",
       "0    шотландцы в национальной одежде с флагом. - ba...   \n",
       "1    королевская золотая корона с драгоценностями н...   \n",
       "2                                                Пасха   \n",
       "3    округа нью-джерси карта печати - elizabeth sto...   \n",
       "4    английская золотая корона с драгоценностями, и...   \n",
       "..                                                 ...   \n",
       "495                                   bokeh Background   \n",
       "496                              Healthcare, Medicine,   \n",
       "497  A set of business teams icons that include edi...   \n",
       "498              Vector floral pattern icon collection   \n",
       "499  Set of Brown ribbons, banners, badges and labe...   \n",
       "\n",
       "                                          blip_caption  \n",
       "0       a man and a woman dressed in scottish clothing  \n",
       "1             a british flag with a crown on top of it  \n",
       "2       a couple of people that are painting some eggs  \n",
       "3                     a map of the state of new jersey  \n",
       "4    a purple and gold crown sitting on top of a wh...  \n",
       "..                                                 ...  \n",
       "495           a blurry image of rain drops on a window  \n",
       "496            a large set of medical and health icons  \n",
       "497                a large set of line icons of people  \n",
       "498  a bunch of different colored circles on a whit...  \n",
       "499                a set of various ribbons and badges  \n",
       "\n",
       "[500 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.df[['text', 'blip_caption']]\n"
   ]
  }
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